Using Deep Learning to Extrapolate Protein Expression Measurements.
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ICR Authors
Authors
Barzine, MP
Freivalds, K
Wright, JC
Opmanis, M
Rituma, D
Ghavidel, FZ
Jarnuczak, AF
Celms, E
Čerāns, K
Jonassen, I
Lace, L
Vizcaíno, JA
Choudhary, JS
Brazma, A
Viksna, J
Freivalds, K
Wright, JC
Opmanis, M
Rituma, D
Ghavidel, FZ
Jarnuczak, AF
Celms, E
Čerāns, K
Jonassen, I
Lace, L
Vizcaíno, JA
Choudhary, JS
Brazma, A
Viksna, J
Document Type
Journal Article
Date
2020-11-01
Date Accepted
2020-09-03
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.
Citation
Proteomics, 2020, 20 (21-22), pp. e2000009 - ?
Source Title
Publisher
WILEY
ISSN
1615-9853
eISSN
1615-9861
Collections
Research Team
Functional Proteomics Group